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Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets
Understanding dysregulation of the eukaryotic initiation factor 4F (eIF4F) complex across tumor types is critical to cancer treatment development. We present a protocol and accompanying R package “eIF4F.analysis”. We describe analysis of copy number status, gene abundance and stoichiometry, survival...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768376/ https://www.ncbi.nlm.nih.gov/pubmed/36595939 http://dx.doi.org/10.1016/j.xpro.2022.101880 |
_version_ | 1784854152736669696 |
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author | Wu, Su Wagner, Gerhard |
author_facet | Wu, Su Wagner, Gerhard |
author_sort | Wu, Su |
collection | PubMed |
description | Understanding dysregulation of the eukaryotic initiation factor 4F (eIF4F) complex across tumor types is critical to cancer treatment development. We present a protocol and accompanying R package “eIF4F.analysis”. We describe analysis of copy number status, gene abundance and stoichiometry, survival probability, expression covariation, correlating genes, mRNA/protein correlation, and protein co-expression. Using publicly available large multi-omics data, eIF4F.analysis permits computationally derived and statistically powerful inferences regarding initiation factor regulation in human cancers and clinical relevance of protein interactions within the eIF4F complex. For complete details on the use and execution of this protocol, please refer to Wu and Wagner (2021).(1) |
format | Online Article Text |
id | pubmed-9768376 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-97683762022-12-22 Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets Wu, Su Wagner, Gerhard STAR Protoc Protocol Understanding dysregulation of the eukaryotic initiation factor 4F (eIF4F) complex across tumor types is critical to cancer treatment development. We present a protocol and accompanying R package “eIF4F.analysis”. We describe analysis of copy number status, gene abundance and stoichiometry, survival probability, expression covariation, correlating genes, mRNA/protein correlation, and protein co-expression. Using publicly available large multi-omics data, eIF4F.analysis permits computationally derived and statistically powerful inferences regarding initiation factor regulation in human cancers and clinical relevance of protein interactions within the eIF4F complex. For complete details on the use and execution of this protocol, please refer to Wu and Wagner (2021).(1) Elsevier 2022-12-12 /pmc/articles/PMC9768376/ /pubmed/36595939 http://dx.doi.org/10.1016/j.xpro.2022.101880 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Protocol Wu, Su Wagner, Gerhard Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets |
title | Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets |
title_full | Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets |
title_fullStr | Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets |
title_full_unstemmed | Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets |
title_short | Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets |
title_sort | protocol to analyze dysregulation of the eif4f complex in human cancers using r software and large public datasets |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768376/ https://www.ncbi.nlm.nih.gov/pubmed/36595939 http://dx.doi.org/10.1016/j.xpro.2022.101880 |
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